{"technology":{"slug":"climate-science","name":"Climate Science","description":"Climate change research, modeling, and mitigation. Covers atmospheric science, carbon capture, climate modeling, tipping points, and adaptation strategies.","discipline":"Earth Science / Environmental","icon":"🌍"},"lastUpdated":"2026-10-26T15:21:58.687Z","articleCount":15,"articles":[{"id":"oa-W2088571921","title":"Comparing niche‐ and process‐based models to reduce prediction uncertainty in species range shifts under climate change","authors":"Xavier Morin, Wilfried Thuiller","journal":"Ecology","pubDate":"2009-05-01","doi":"10.1890/08-0134.1","abstract":"Obtaining reliable predictions of species range shifts under climate change is a crucial challenge for ecologists and stakeholders. At the continental scale, niche-based models have been widely used in the last 10 years to predict the potential impacts of climate change on species distributions all over the world, although these models do not include any mechanistic relationships. In contrast, species-specific, process-based predictions remain scarce at the continental scale. This is regrettable because to secure relevant and accurate predictions it is always desirable to compare predictions derived from different kinds of models applied independently to the same set of species and using the same raw data. Here we compare predictions of range shifts under climate change scenarios for 2100 derived from niche-based models with those of a process-based model for 15 North American boreal and temperate tree species. A general pattern emerged from our comparisons: niche-based models tend to predict a stronger level of extinction and a greater proportion of colonization than the process-based model. This result likely arises because niche-based models do not take phenotypic plasticity and local adaptation into account. Nevertheless, as the two kinds of models rely on different assumptions, their complementarity is revealed by common findings. Both modeling approaches highlight a major potential limitation on species tracking their climatic niche because of migration constraints and identify similar zones where species extirpation is likely. Such convergent predictions from models built on very different principles provide a useful way to offset uncertainties at the continental scale. This study shows that the use in concert of both approaches with their own caveats and advantages is crucial to obtain more robust results and that comparisons among models are needed in the near future to gain accuracy regarding predictions of range shifts under climate change.","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W2088571921","citationCount":497,"isOpenAccess":false,"pdfUrl":""},{"id":"oa-W2112933898","title":"Should we believe model predictions of future climate change?","authors":"Reto Knutti","journal":"Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences","pubDate":"2008-09-25","doi":"10.1098/rsta.2008.0169","abstract":"Predictions of future climate are based on elaborate numerical computer models. As computational capacity increases and better observations become available, one would expect the model predictions to become more reliable. However, are they really improving, and how do we know? This paper discusses how current climate models are evaluated, why and where scientists have confidence in their models, how uncertainty in predictions can be quantified, and why models often tend to converge on what we observe but not on what we predict. Furthermore, it outlines some strategies on how the climate modelling community may overcome some of the current deficiencies in the attempt to provide useful information to the public and policy-makers.","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W2112933898","citationCount":279,"isOpenAccess":false,"pdfUrl":""},{"id":"oa-W2515542550","title":"Genetically informed ecological niche models improve climate change predictions","authors":"Dana H. Ikeda, Tamara L. Max, Gerard J. Allan, Matthew K. Lau, Stephen M. Shuster, Thomas G. Whitham","journal":"Global Change Biology","pubDate":"2016-08-20","doi":"10.1111/gcb.13470","abstract":"We examined the hypothesis that ecological niche models (ENMs) more accurately predict species distributions when they incorporate information on population genetic structure, and concomitantly, local adaptation. Local adaptation is common in species that span a range of environmental gradients (e.g., soils and climate). Moreover, common garden studies have demonstrated a covariance between neutral markers and functional traits associated with a species' ability to adapt to environmental change. We therefore predicted that genetically distinct populations would respond differently to climate change, resulting in predicted distributions with little overlap. To test whether genetic information improves our ability to predict a species' niche space, we created genetically informed ecological niche models (gENMs) using Populus fremontii (Salicaceae), a widespread tree species in which prior common garden experiments demonstrate strong evidence for local adaptation. Four major findings emerged: (i) gENMs predicted population occurrences with up to 12-fold greater accuracy than models without genetic information; (ii) tests of niche similarity revealed that three ecotypes, identified on the basis of neutral genetic markers and locally adapted populations, are associated with differences in climate; (iii) our forecasts indicate that ongoing climate change will likely shift these ecotypes further apart in geographic space, resulting in greater niche divergence; (iv) ecotypes that currently exhibit the largest geographic distribution and niche breadth appear to be buffered the most from climate change. As diverse agents of selection shape genetic variability and structure within species, we argue that gENMs will lead to more accurate predictions of species distributions under climate change.","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W2515542550","citationCount":227,"isOpenAccess":false,"pdfUrl":""},{"id":"oa-W2023971153","title":"Regional climate‐model predictions of extreme rainfall for a changing climate","authors":"Chris Huntingford, Richard Jones, Christel Prudhomme, Rob Lamb, J. H. C. Gash, David A. Jones","journal":"Quarterly Journal of the Royal Meteorological Society","pubDate":"2003-04-01","doi":"10.1256/qj.02.97","abstract":"Abstract Major floods occurred in the United Kingdom during autumn 2000. These were caused by a rapid sequence of heavy rainfall events that occurred over a period of many weeks leading to record‐breaking monthly‐to‐seasonal rainfall totals. The question was raised as to whether such rainfall events may be related to human‐induced climate change. Climate‐model predictions of future changes in mean precipitation behaviour are well established. However, to understand flooding requires an examination of predictions of extreme rainfall behaviour at a relatively small spatial scale. For three areas within the United Kingdom, output from a Hadley Centre regional climate model, ‘nested’ within one of its general‐circulation models, is compared with raingauge data averaged over these areas for the period 1961–1990. This shows that the modelling system is good at predicting the statistical likelihood of extreme rainfall events seen in historical data. This result holds for extreme rainfall totals over daily to monthly timescales. When the modelling system is used to predict changes in these extreme events resulting from atmospheric CO2 concentrations that may be representative of the period 2080–2100, significant reductions in the return periods of such events are seen. For example, 30‐day rainfall totals, which happened in the recent past on average once in 20 years, are predicted to happen once in 3–5 years. An interpolation method based upon climate‐model output and incorporating raingauge data is used to estimate how rainfall extremes may have changed between the middle of the 19th century, and for a period centred on the year 2000. This also predicts that increased greenhouse gases have led to reduced return periods of extreme rainfall events for three sites of interest, though in this case the changes are not statistically significant. © Royal Meteorological Society, 2003. R. G. Jones's contribution is Crown copyright","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W2023971153","citationCount":149,"isOpenAccess":false,"pdfUrl":""},{"id":"oa-W1967971435","title":"Anthropocene changes in desert area: Sensitivity to climate model predictions","authors":"N. M. Mahowald","journal":"Geophysical Research Letters","pubDate":"2007-09-01","doi":"10.1029/2007gl030472","abstract":"Changes in desert area due to humans have important implications from a local, regional to global level. Here I focus on the latter in order to better understand estimated changes in desert dust aerosols and the associated iron deposition into oceans. Using 17 model simulations from the World Climate Research Programme's Coupled Model Intercomparison Project phase 3 multi‐model dataset and the BIOME4 equilibrium vegetation model, I estimate changes in desert dust source areas due to climate change and carbon dioxide fertilization. If I assume no carbon dioxide fertilization, the mean of the model predictions is that desert areas expand from the 1880s to the 2080s, due to increased aridity. If I allow for carbon dioxide fertilization, the desert areas become smaller. Thus better understanding carbon dioxide fertilization is important for predicting desert response to climate. There is substantial spread in the model simulation predictions for regional and global averages.","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W1967971435","citationCount":111,"isOpenAccess":false,"pdfUrl":""},{"id":"oa-W2751105820","title":"Modelling of the carbon sequestration and its prediction under climate change","authors":"Vilém Pechanec, Jan Purkyt, Antonín Benc, Chukwudi Nwaogu, Lenka Štěrbová, Pavel Cudlín","journal":"Ecological Informatics","pubDate":"2017-09-01","doi":"10.1016/j.ecoinf.2017.08.006","abstract":"","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W2751105820","citationCount":91,"isOpenAccess":false,"pdfUrl":""},{"id":"oa-W2129530276","title":"Probabilistic climate change predictions applying Bayesian model averaging","authors":"Seung‐Ki Min, Daniel Simonis, Andreas Hense","journal":"Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences","pubDate":"2007-06-14","doi":"10.1098/rsta.2007.2070","abstract":"This study explores the sensitivity of probabilistic predictions of the twenty-first century surface air temperature (SAT) changes to different multi-model averaging methods using available simulations from the Intergovernmental Panel on Climate Change fourth assessment report. A way of observationally constrained prediction is provided by training multi-model simulations for the second half of the twentieth century with respect to long-term components. The Bayesian model averaging (BMA) produces weighted probability density functions (PDFs) and we compare two methods of estimating weighting factors: Bayes factor and expectation-maximization algorithm. It is shown that Bayesian-weighted PDFs for the global mean SAT changes are characterized by multi-modal structures from the middle of the twenty-first century onward, which are not clearly seen in arithmetic ensemble mean (AEM). This occurs because BMA tends to select a few high-skilled models and down-weight the others. Additionally, Bayesian results exhibit larger means and broader PDFs in the global mean predictions than the unweighted AEM. Multi-modality is more pronounced in the continental analysis using 30-year mean (2070-2099) SATs while there is only a little effect of Bayesian weighting on the 5-95% range. These results indicate that this approach to observationally constrained probabilistic predictions can be highly sensitive to the method of training, particularly for the later half of the twenty-first century, and that a more comprehensive approach combining different regions and/or variables is required.","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W2129530276","citationCount":91,"isOpenAccess":false,"pdfUrl":""},{"id":"oa-W2105992247","title":"On the importance of paleoclimate modelling for improving predictions of future climate change","authors":"J. C. Hargreaves, J. D. Annan","journal":"Climate of the past","pubDate":"2009-12-21","doi":"10.5194/cp-5-803-2009","abstract":"Abstract. We use an ensemble of runs from the MIROC3.2 AGCM with slab-ocean to explore the extent to which mid-Holocene simulations are relevant to predictions of future climate change. The results are compared with similar analyses for the Last Glacial Maximum (LGM) and pre-industrial control climate. We suggest that the paleoclimate epochs can provide some independent validation of the models that is also relevant for future predictions. Considering the paleoclimate epochs, we find that the stronger global forcing and hence larger climate change at the LGM makes this likely to be the more powerful one for estimating the large-scale changes that are anticipated due to anthropogenic forcing. The phenomena in the mid-Holocene simulations which are most strongly correlated with future changes (i.e., the mid to high northern latitude land temperature and monsoon precipitation) do, however, coincide with areas where the LGM results are not correlated with future changes, and these are also areas where the paleodata indicate significant climate changes have occurred. Thus, these regions and phenomena for the mid-Holocene may be useful for model improvement and validation.","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W2105992247","citationCount":25,"isOpenAccess":true,"pdfUrl":"https://www.clim-past.net/5/803/2009/cp-5-803-2009.pdf"},{"id":"oa-W2075542465","title":"Prediction of Maize Yield Response to Climate Change with Climate and Crop Model Uncertainties","authors":"Yi Zhang, Yanxia Zhao, Sining Chen, Jianping Guo, Enli Wang","journal":"Journal of Applied Meteorology and Climatology","pubDate":"2015-03-10","doi":"10.1175/jamc-d-14-0147.1","abstract":"Abstract Projections of climate change impacts on crop yields are subject to uncertainties, and quantification of such uncertainty is essential for the effective use of the projection results for adaptation and mitigation purposes. This work analyzes the uncertainties in maize yield predictions using two crop models together with three climate projections downscaled with one regional climate model nested with three global climate models under the A1B emission scenario in northeast China (NEC). Projections were evaluated for the Zhuanghe agrometeorological station in NEC for the 2021–50 period, taking 1971–2000 as the baseline period. The results indicated a yield reduction of 13% during 2021–50, with 95% probability intervals of (−41%, +12%) relative to 1971–2000. Variance decomposition of the yield projections showed that uncertainty in the projections caused by climate and crop models is likely to change with prediction period, and climate change uncertainty generally had a larger impact on projections than did crop model uncertainty during the 2021–50 period. In addition, downscaled climate projections had significant bias that can introduce significant uncertainties in yield projections. Therefore, they have to be bias corrected before use.","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W2075542465","citationCount":24,"isOpenAccess":false,"pdfUrl":""},{"id":"oa-W2022264511","title":"Certainty and Uncertainty in Climate Change Predictions: What Use are Climate Models?","authors":"Andrew James Watson","journal":"Environmental and Resource Economics","pubDate":"2007-12-21","doi":"10.1007/s10640-007-9173-9","abstract":"","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W2022264511","citationCount":23,"isOpenAccess":false,"pdfUrl":""},{"id":"oa-W2057890437","title":"Climate Modelling, Uncertainty and Responses to Predictions of Change","authors":"Ann Henderson-Sellers","journal":"Mitigation and Adaptation Strategies for Global Change","pubDate":"1996-01-01","doi":"10.1007/bf00625613","abstract":"","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W2057890437","citationCount":13,"isOpenAccess":false,"pdfUrl":""},{"id":"oa-W4283070034","title":"Climate Change Prediction Using ARIMA Model","authors":"Navaneetha Krishnan M, R Ranjith, B Lavanya","journal":"International Journal for Research in Applied Science and Engineering Technology","pubDate":"2022-06-17","doi":"10.22214/ijraset.2022.43777","abstract":"Abstract: It is a challenging task to forecast weather data accurately. The temperature change has important implications for business and economic activity. Effective management of global climate change impacts will depend upon accurate and costeffective forecasts. This paper univariate statistic techniques to model the properties of a world mean temperature dataset to develop a parsimonious forecasting model for managerial decision-making over the short-term horizon and the ARIMAbased prognostication tool has been developed by implementing the ARIMA algorithm in python. Although the model is estimated on global temperature data, the methodology could even be applied to temperature data at more localized levels. The statistical techniques include seasonal and non-seasonal unit root testing with and without structural breaks as well as ARIMA and SARIMA modelling. This paper helps us to predict the air temperature, which is the main problem of global warming. Prediction of the likely impact of climate change on monthly mean maximum and minimum temperature in Tamilnadu. Time-series techniques to develop a parsimonious model of global mean temperature change that can be used to forecast over the short-term horizon (5- 10) years. Keywords: Global warming, Forecasting, temperature","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W4283070034","citationCount":8,"isOpenAccess":true,"pdfUrl":"https://doi.org/10.22214/ijraset.2022.43777"},{"id":"oa-W2007202828","title":"Consistency in Global Climate Change Model Predictions of Regional Precipitation Trends","authors":"Bruce T. Anderson, Catherine Reifen, Ralf Toumi","journal":"Earth Interactions","pubDate":"2009-08-10","doi":"10.1175/2009ei273.1","abstract":"Abstract Projections of human-induced climate change impacts arising from the emission of atmospheric chemical constituents such as carbon dioxide typically utilize multiple integrations (or ensembles) of numerous numerical climate change models to arrive at multimodel ensembles from which mean and median values and probabilities can be inferred about the response of various components of the observed climate system. Some responses are considered reliable in as much as the simulated responses show consistency within ensembles and across models. Other responses—particularly at regional levels and for certain parameters such as precipitation—show little intermodel consistency even in the sign of the projected climate changes. The authors’ results show that in these regions the consistency in the sign of projected precipitation variations is greater for intramodel runs (e.g., runs from the same model) than intermodel runs (e.g., runs from different models), indicating that knowledge of the internal “dynamics” of the climate system can provide additional skill in making projections of climate change. Given the consistency provided by the governing dynamics of the model, the authors test whether persistence from an individual model trajectory serves as a good predictor for its own behavior by the end of the twenty-first century. Results indicate that, in certain regions where intermodel consistency is low, the short-term trends of individual model trajectories do provide additional skill in making projections of long-term climate change. The climate forcing for which this forecast skill becomes relatively large (e.g., correct in 75% of the individual model runs) is equivalent to the anthropogenic climate forcing imposed over the past century, suggesting that observed changes in precipitation in these regions can provide guidance about the direction of future precipitation changes over the course of the next century.","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W2007202828","citationCount":3,"isOpenAccess":true,"pdfUrl":"https://journals.ametsoc.org/downloadpdf/journals/eint/13/9/2009ei273.1.pdf"},{"id":"oa-W4282571504","title":"CLIMATE CHANGE MODELLING: PREDICTION OF AIR QUALITY INDEX IN ALMATY CITY","authors":"Elvira Apsalyamova","journal":"Internauka","pubDate":"2022-04-23","doi":"10.32743/26870142.2022.14.237.336996","abstract":"CLIMATE CHANGE MODELLING: PREDICTION OF AIR QUALITY INDEX IN ALMATY CITY Elvira Apsalyamova Master of Faculty of Information Technology, Kazakh-British Technical University, Kazakhstan, Almaty ABSTRACT Air quality topic is a","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W4282571504","citationCount":0,"isOpenAccess":true,"pdfUrl":"https://doi.org/10.32743/26870142.2022.14.237.336996"},{"id":"oa-W7144320622","title":"Big Data Analytics for Climate Change Modeling and Prediction","authors":"Dr. Richard Hughes","journal":"American Journal of Data Science and Analysis","pubDate":"2020-08-31","doi":"10.71465/ajdsa884","abstract":"The role of Big Data analytics in climate change modeling and prediction has become increasingly important in recent years. With vast amounts of environmental data being generated from satellites, sensors, and climate models, Big Data techniques such as machine learning and data mining are essential for processing and analyzing this information. This article explores how Big Data analytics contributes to climate change modeling, the challenges faced, and the future potential for improving climate predictions, decision-making, and policy development","tldr":"","source":"OpenAlex","sourceUrl":"https://openalex.org/W7144320622","citationCount":0,"isOpenAccess":true,"pdfUrl":"https://australiansciencejournals.com/ajdsa/article/download/884/986"}],"links":{"web":"https://science-database.com/technology/climate-science","llms_txt":"https://science-database.com/technology/climate-science/llms.txt","api":"https://science-database.com/api/v1/technology/climate-science"}}